Risk Prediction of Child Vaccination Based on Knowledge Graph and Pre-trained Language Model
Primary healthcare providers lack the ability to assess the risk of vaccination for children with certain illnesses.It is a viable solution to developing a risk prediction model for pediatric vaccination,by leveraging the experience of healthcare professionals in tertiary hospitals,to assist primary healthcare providers in swiftly identifying high-risk pediatric patients.This study proposes an intelligent method for vaccine recommendations based on a knowledge graph.Firstly,a method for medical named entity recognition called ELECTRA-BiGRU-CRF,based on pre-trained language models,is proposed for named entity extraction from outpatient electronic medical records.Secondly,a vaccination ontology is designed,with relationships and attributes defined,to construct a Chinese childhood vaccination knowledge graph based on Neo4j.Finally,a method for vaccine recommendations guided by significant categories using pre-trained language models is proposed based on the constructed knowledge graph.Experimental results indicate that the proposed methods can provide diagnostic assistance to physicians and offer support for deciding whether vaccines can be administered to children with certain illnesses.
Chinese electronic medical recordpre-trained language model(PLM)knowledge graphnamed entity recognition(NER)vaccination recommendation
NETL
NSTL
万方数据
